Log Management articles are gathered in a special category in our blog to help our community of readers to tackle issues like security, performance, health, compliance, and more.

We also focus on issues such as ROI, extracting value from log management tools, how to add functionality to open source tools like ELK, using out of the box tools like XpoLog.

We cover log management use cases for a different type of logs.

We wish to help you easily understand errors, problems, trends, patterns, detect risks, exceptions, anomalies across your entire environment and help you extract actionable insights out of your log data.

We bring experts from diverse fields to write articles to overcome different challenges and daily requirements.

Our articles are very practical and don’t focus on XpoLog users only.

We aim to deliver value to any IT, DevOps, Security, professional.

What Is IT Risk Management? A Leader’s Introductory Guide

Do you own or manage a business? Managing a company involves many steps. These steps begin right from the genesis of an idea through its execution. They also involve choosing a development procedure and managing risks. Most software companies these days are adopting the latest project methodologies, like DevOps and agile. After all, you have to keep up with changing trends and implement them in your own life—but you also have to manage the risks associated with information technology (IT) and tackle them to manage your business. So, you’re probably wondering, what is IT risk management?

That’s what we’ll be talking about today. In this post, I’ll guide you on what constitutes risk in IT. I’ll also explain why you need to understand risk in a business atmosphere. After that, we’ll examine the different steps involved in IT risk management. And finally, we’ll discuss how you can minimize risk at the workplace. So why wait? Let’s kick-start this guide.

What Is Risk Management?

The first step in learning about risk management is understanding what “risk” means. “Risk” refers to any kind of threat to IT management. Management issues, human errors, accidents, and financial and legal issues are examples of risks. For instance, a hardware issue that wipes out a huge amount of data is a risk. The influence of risk can stretch to not one but a variety of mediums in the work environment. Hence, we need to know how to manage risk in IT.

Risk management is the control and management of threats in the workplace. There is a set of processes that involves analyzing and assessing different types of risks. A risk analyst’s job is to bring out positive solutions that may reduce the chance that a risk occurs. Apart from that, risk management includes complying with new regulations as trends change. But why is risk management important? Let’s take a look at the next section.

Why Do We Need Risk Management in IT?

Risks can come from external as well as internal sources. As I noted above, a hardware failure causing data loss can be a risk. Similarly, data theft by hackers or malware is another risk. Hence, it is crucial to manage these risks. The threats include financial and logistical issues, human errors, and more. If these threats surround your company, you risk losing money or your reputation.

Risk management helps you save money and resources. If you identify, plan, and execute solutions, the number of risks can decrease. So, you must include risk management as one of your company’s key objectives. This will save time for everyone in the company. You may wonder if it’s important to tackle every risk that comes your way. If you don’t, the risks can impact your employees as well.

Can you control risks in your company? In the next section, I’ll guide you on the various steps involved in risk management. If you follow them step by step, you can expect fewer threats to your company. The steps will also help you produce more solutions. So, let’s proceed with a detailed guide that will help you control risks.

What Are the Risk Management Steps in an IT Company?

How can you manage risks in IT? Risk management is an ongoing cycle. It includes the key elements of identifying, testing, and executing to fulfill it. If you find solutions to risks, you can remove them from your organization.

Risk management involves six steps that are important for every IT company.

Identify the Scope of the Problem

Firstly, map out the necessary outline to follow before starting the process. This outline should include the objectives of the process along with the employees’ goals. Once you’ve done that, move on to the next step: identifying the problem.

Identify the Risk

First, ask yourself what can go wrong. If there are any answers, those are the potential risks. You have to identify them to decide how to remove them in a situation. The main source of the problem in an event is your biggest risk. If you can’t identify the risk here, it will harm you later.

For instance, suppose you store your data in a data center. In that case, what’s the biggest risk? Obviously, the answer is data security, because poor security measures will lead to data theft.

Now that we’ve identified the risk, let’s move on to the next step.

Analyze the Risk

Can a risk affect us? This is the first question that comes up when you identify risks in IT. Risks are assessed based on their frequency, but you can measure them based on loss as well. You can make a good guess whether a risk is harmful or not. This will impact your company accordingly. Therefore, you need to be experienced to make these guesses. Risk evaluation can be tough because data isn’t easily available. It can also be based on asset evaluation. But if you don’t try to test your guess, you’ll never learn. IT companies rely on risk analysts for this step. We’ll discuss how to control the risk in the next step.

Treat the Risk

What should you do when you see a risk? You can either eliminate the risk or control it. You must understand how to treat risks. Based on your decision, make a reusable plan to consider the next time similar risks occur. If the risk causes loss, remove the risk or control the loss to avoid any threats. Making a smart plan is key to treating risks in the workplace. For instance, let’s explore the data center security issue we discussed earlier. How can we control the risk of data security? Advanced security like restricted-access CCTV monitoring is the answer.

Is that all you need to manage risks? Well, unfortunately not. In the next steps, we’ll discuss how to safeguard your plan.

Execute the Plan

Apply the steps you’ve planned to check for positive results. If you’re unable to mitigate the risk with your plan, repeat the first four steps and create a new plan. Risk management plans can have a few errors before success. Ensure that you don’t sacrifice the company’s goal while making your plan. If the plan works out well, you can move on to the last step of risk management.

Review the Plan

You can expect to face failures in risk management. Not every plan will be perfect. Hence, you should research effective control strategies and add them to your plan. This will help to make the plan better for the next time you find a threat to your company.

As I mentioned earlier, the steps to risk management are important to achieve a company’s goal. Business environments continue to change. You have to stay up to date with the trends to find strategies for effective risk management.

But, what strategies can you apply to manage risk? We’ll discuss them in the next and final section of this post.

Strategies to Minimize Risks in IT

An IT company will need different ways to manage risks. Most of these strategies include a good work culture. If the people in your company are happy, you can minimize risks. Let’s discuss the strategies you need to follow to manage or reduce risk.

Lead by Example

Safety measures aren’t just learned but demonstrated. You should promote a safe environment for all employees. With the advent of any threat, the team will be better prepared to face the risk. What kinds of threats might you face? Well, that varies from company to company. All in all, you should establish protocols so that all employees can prepare themselves. Not only that, arrange training sessions for your employees to make them aware of safety protocols and ensure that they follow the rules and protocols.

Bond With Your Employees

Do you get along with your employees and coworkers? If the answer is no, you should work on improving relations. Employee bonding is important in a company. It builds strong relations, support, and loyalty in the workplace. You can manage risks better if everyone works together. The time it takes to tackle a risk will lessen. Not only that, if you’re friendly with your employees, they’ll be more productive. You can also try to make employees happy by showing the value of their work from time to time. Risk management becomes easier when everyone feels like part of the company.

Keep the Workplace Healthy

Does everyone at work stay healthy? Do all employees have healthy habits? A healthy work culture promotes productivity. So, check up on everyone and see if they’re happy. If someone is unhappy with their job, sit with them and listen to their problems. You can tackle the risks in a company only when everyone is willing to work for the company. After all, health is wealth!

Make Smart Decisions

Regardless of whether a risk is virtual or physical, you need to act quickly in response and avoid prolonging a threat. If there’s a loss of data, look for backup. If someone is hurt, call for medical help. Managing risks in a company is important for growth. The best way to manage risks in IT is by making smart decisions that benefit everyone.

What’s Your Role in Risk Management?

Ultimately, your role is to be aware of threats from the start. IT companies should have many solutions for risks. You must discover the risks and remove them. You can reduce them by using the correct techniques. In the big picture, your losses can cost your company. Good risk management is all about minimizing costs. Learn how to manage risks, and you can turn the losses into profits.

This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.

DevOps Incident Management: A Guide With Best Practices

This is the one post I hope you’ll never need. However, should you ever need it, this is your one-stop shop for understanding how to proceed with DevOps incident management. Have you just been attacked? Did the commit go wrong? A CI pipeline went haywire? Don’t worry. I got you.

I know there’s plenty of material out there already, but what makes this post different is that we’ll discuss the how-tos, namely, the steps to follow in DevOps incident management, as well as the best practices. As a DevOps manager, you understand how important it is to maintain a secure infrastructure. Combine a knowledgeable team with state-of-the-art infrastructure and a proper DevOps workflow, and you’re ready to tackle whatever comes your way.

It all begins with understanding what it all means. DevOps is not just a meaningless list of steps to follow. It combines people, actions, and tools to create workflows and responses. Moreover, it’s almost a spirit that drives the development life cycle forward in a safe, controlled, and efficient way. However, incidents do happen, and we’ll go over what to do about them.

So, what’s an incident? Your company may define it differently, but in essence, it’s when something happened to the data or the infrastructure you manage. The term, as well as the causes, can be described that loosely. The consequences, however, are not: server downtime, security breach, data corruption.

Having discussed the basics here, let’s jump into the steps to actually fix things.

The Steps

As I mentioned before, as soon as there’s an incident, there are five well-known steps to follow. Unfortunately, as smart as I want to seem, I didn’t come up with them. And although they’re easily accessible, I think they’re due for a refresh. That is, these well-known concepts have been around since the late 2000s, and since then, the applications and concepts have changed drastically. While they’re very useful, you always need to remember that there’s no one-size-fits-all solution.

So, what are the five steps to DevOps incident management?

1. Detection

Firstly, early detection is critical. Your log monitoring comes to play an important part at this stage. Similarly, it’s important to remember how key it is to set up your metrics and alert systems. You can set up alerts on a number of different things. Server monitoring includes network usage, CPU and/or GPU usage, HDD capacity, etc. A constant collaboration culture will allow your organization to define what to look for. False positives are always a thing.

The crucial part to understand is that as soon as you detect an incident—whether by yourself or using a monitoring tool—a ticket or event should be recorded adequately by the team. Now, you’re ready to respond.

2. Response

At this point, the DevOps team starts to receive all of the information coming from the monitoring tools. Normally, there’s an on-call team, leading what needs to be done. In simpler terms, that means rerouting the information to the proper channels.

The exact channels depend on your company’s regular day-to-day operations. Some people might use the ever-popular Slack to communicate. Fancy a Microsoft solution? Microsoft Teams will work just as well. And, of course, if you’re lucky enough to all be in the same place, reaching out to the relevant parties and gathering them in a conference room will be the way to go.

But who exactly do you reach out to?

  • Customer support
  • Product management
  • Engineers

Or in more extreme cases (I hope this isn’t you), you might contact these groups:

  • Digital forensics
  • Security and upper management

Hopefully, your issue is properly channeled and responsibilities have been assigned. Now, it’s time to do some problem-solving.

3. Resolution

Now let’s fix what happened. At this point, you, as incident manager (IM), have all the tools to understand what happened, but more importantly, you should be able to provide context. Following that, it’s time to address the situation and limit its potential setbacks. Was cybersecurity involved? That always takes priority. In this stage, systems and data are recovered, and applications should return to normal.

Collaboration is very important. Your sysadmins and engineers should work together to respond to tickets and commit patches that remedy the situation. If required, the database should probably be restored from a backup.

Good news though—you’ve made it this far. The next step is to learn from our mistakes and retrace our steps to analyze what happened.

4. Closure

Also called the analysis stage, this is where we commit to learning from what happened. As IM, the job’s on you to gather (even more) information and metrics from the relevant members of the response team—they’ll come in handy.

I can’t stress enough that this stage should be blameless at its essence. Pointing fingers never helps anybody, and it’s considered a reactive move, completely the opposite of a proactive measure, which should be the way to go. What’s more important, at this stage, we assign responsibilities to prevent this situation from happening again. Also, at this point, the tickets are closed.

The solution should also be coordinated and framed within the organization’s security policy. Now you’re ready for the last step, which involves including the fix as part of the continuous integration strategy.

5. Improvement/Readiness/Integration

The name should explain it all by itself. These are the proactive measures your company takes to prevent the incident from reoccurring. Moreover, this is particularly important for your providers and users out there. Post-incident information is processed and sent to the engineering managers; this way, they prioritize permanent fixes along the development pipeline.

Best Practices

In addition to understanding the steps to take, the second most important thing is to know the best practices out there. DevOps can be confusing, and everybody seems to be an expert. Here are a few tips.

Don’t take the one-tool approach. This involves getting your team used to tracking and monitoring on different tools. Basically, tracking just servers isn’t enough. Application logs are important too.

As a matter of fact, following a 360-degree approach will serve you even better. Looking for ideas? XPLG offers a great product that speaks to a number of different tools and log formats. Their extensive marketplace allows you to connect with hundreds of stacks out there.

Hand in hand with the above, keeping a safe balance between under- and over-alerting will keep everyone happy. Your team won’t appreciate an irrelevant alert in the middle of the night. They want alerts of higher importance and that they can do something with. In a word, actionable. Even better, some companies develop their own systems to assess severity as issues arise.

Finally, assess your security policies. Having an updated security policy is key. In fact, it determines how your company will react to an event and reassures your users that their data is safe. Moreover, talk about how you comply with the latest in security practices out there.


Responsibility. As a potential IM, you need to know firsthand who’s who. That will make it easier to assign incident management roles. A common set of roles for companies out there includes the following:

  • Communications leader: Undeniably, this is the key person who serves as the liaison between the IM and the business and tech parties.
  • Tech leader: The person in this position is in charge of assessing the situation from a technical level and applying the proper fixes in a timely manner.
  • Engineering manager: This position is normally present beforehand. Typically, this person is in charge of following up with the post-incident reports. If the tech leader is in charge of the investigation, the engineering manager takes care of the long-term remediation.

Wrapping Up

Defining metrics and procedures should be part of every DevOps team. I hope this post becomes the one resource you need to evaluate how your company is doing and how it approaches DevOps incident management. Let’s be clear—incidents can happen. In the era of data science, your data and monitoring should drive the operations.

What are you waiting for to get started?

DevOps Metrics: 7 KPIs to Evaluate Your Team’s Maturity

Measuring the maturity of your DevOps team might sound difficult, but it isn’t at all. Simple key performance indicators (KPIs), such as the deployment success rate or mean time between failure, give a good indication of the maturity of your DevOps team. By “mature,” I mean that your team consistently and smoothly operates at a high level and can deploy several times a day with very little risk.

This article will answer these questions:

  • What’s a mature DevOps team really like?
  • How does a new DevOps team organize itself?
  • What are the phases of DevOps maturity?
  • What metrics should a DevOps team measure?

Let’s get started.

Elements That Define DevOps Maturity

It’s possible to measure DevOps maturity in many ways. Maturity includes cultural, technical, and process-related elements. And you can measure all of these through DevOps KPIs or metrics.

For example, on a cultural level, you’ll want to learn how DevOps engineers share knowledge among team members. An active environment of knowledge sharing is a good sign that your team works well together.

On a technical level, you’ll want to measure DevOps KPIs related to errors, mean time to repair a bug, or the availability of a service. It’s much easier to measure these technical metrics than it is to measure cultural elements.

Last, let’s have a look at process metrics. These are mostly concerned with measuring the time needed for completing certain tasks, such as spinning up a new instance of a service. How fast can a DevOps engineer do this? For a recently formed DevOps team, the required time to accomplish such a task might be much higher than it is for a mature team. That’s because an immature team is often still working on standardizing and optimizing processes.

Next, let’s discuss the different phases of maturity for a DevOps team.

Phases of DevOps Maturity

Tuckman’s stages of group development accurately describes what a newly formed DevOps team experiences. When a new team gets together, all team members go through the following four phases:

  • Forming
  • Storming
  • Norming
  • Performing

How do these phases translate to a DevOps team? Let’s find out.

Forming: Need for Leadership

During the forming phase, there’s a lack of clarity, which means the team needs a leader who can provide guidance and strategy. Often during this stage, you either won’t find any DevOps implementation or there’ll be a bare minimum.

During this phase, the team starts to explore possible ways of test automation and different tools for implementing continuous integration (CI). At this stage, DevOps engineers write simple scripts that help automate repeating tasks. It’s safe to say you’ll find an immature team at this stage.

Storming: Fundamental Progress

Next up is the forming stage, which matters because it reveals the team’s initial progress. The team members try to establish fundamentals, such as implementing a simple CI flow with integrated test automation.

At this stage, developers can push their code to the continuous integration (CI) pipeline and receive valuable feedback about it. Often at this stage, you’ll find a strong change toward DevOps culture. This means that basic DevOps tools such as a CI pipeline have been implemented with some basic test automation. Still, you won’t find much of a focus on defining KPIs because the DevOps team is still in the process of building a strong DevOps tooling baseline.

Norming: Independence and Shared Ownership

Next, the norming phase brings the team clear responsibilities and direction. It’s possible to delegate smaller decisions to team members. A change toward agile management of the team happens because the team no longer needs to consult its leader for every decision. Strong independence and a feeling of shared ownership by the team often emerge at this stage.

The norming phase is a time of strong automation—from building the code to testing the automation and code deployments. At this stage, you’ll often find a happy development team whose members are able to improve their efficiency through the integrated toolchain. Because of this, the team can establish continuous delivery.

Moreover, the DevOps team also implements monitoring as part of this phase. Through monitoring, team members can set different KPIs to measure the health of the DevOps team as well as its code and deployments.

Performing: Time for Fine-Tuning

Finally, we’ve arrived at the performing phase. A successful implementation of DevOps is a hallmark of this phase.

During this phase, you’ll find room for experimenting. The team finds optimizations through experimentation. Standardized processes have been established, and there’s an active atmosphere of knowledge sharing among team members.

A team that’s in the performing phase focuses on improving important metrics, such as availability or reducing the error rate.

Now that you understand the different phases of DevOps maturity, what are some ways you can measure that maturity?

7 DevOps Metrics You Should Measure

Let’s take a look at seven of the most important DevOps KPIs for measuring a team’s maturity.

MTTF: Mean Time to Failure

First of all, MTTF refers to the time until the next failure. For example, how often does your deployment cause a failure? Ideally, you’ll want the MTTF metric to be as low as possible. A high MTTF rate can indicate problems with the quality of your software. For example, you may not have enough tests covering different scenarios that might contain bugs.

MTTD: Mean Time to Detect

Next, MTTD is an important KPI for a DevOps team. It tells the team how long it takes before they detect an issue. Immature teams require quite some time to detect issues because they have no monitoring implemented. This means it’s much harder for an immature DevOps team to replay events leading up to an issue. They don’t have any data to fall back on.

In contrast, a more mature team that has monitoring implemented can detect issues faster through the data that team members capture, such as logs or performance data.

MTTR: Mean Time to Repair

MTTR refers to the time needed to fix an issue or error. An immature team might not have much experience and knowledge on the system, which means they’ll likely end up with a high average time.

However, a team at the performing stage won’t need much time to repair incidents. Why? That team has already gathered a lot of knowledge about the DevOps implementation and has been actively sharing knowledge about common incidents. It’s very likely the team has a ready-made solution to the problem.

MTBF: Mean Time Between Failures

The MTBF metric is the most straightforward one. It refers to the average time it takes for a component to fail. This metric is especially useful to determine how stable a particular component in your codebase is. If a particular component fails relatively quickly compared with other components, then you might want to validate the code or architecture for this component. Obviously, the goal is to have components that rarely fail!

Deployment Success Rate

Next, the deployment success rate calculates the rate of successful and unsuccessful deployments. This success rate should be as high as possible for mature teams.

You can improve the deployment success rate by automating and standardizing the deployment process. A higher deployment success ratio will reduce frustrations among team members and create a less stressful job experience.

Deployment Frequency

A high deployment frequency can be an indicator of an optimized CI pipeline. In addition, the ability to deploy frequently allows for a more agile approach toward the development team. Mature DevOps teams often have a high deployment frequency because they have their processes streamlined and standardized.

Next, let’s take a look at the importance of measuring the error rate.

Error Rate

Last, the error rate tells the DevOps team how often new bugs pop up in running applications. It’s important to capture spikes in the error rate because these can indicate that something isn’t right. For example, there might be a database that’s being overloaded with SQL requests, and the DevOps infrastructure isn’t able to scale as quickly as needed.

Here, log analysis can help the team detect such error spikes. Moreover, log analysis lets you measure the number of error log messages.

(Do you want to learn more about log analysis? Check out XPLG’s blog about log forensics.)

Maturity Through DevOps Metrics

In short, metrics are an important instrument to measure the maturity of your DevOps team. Make sure you understand the different phases of team formation through Tuckman’s model. It allows you to better understand how the team functions in each stage.

When measuring metrics, try to start simply, with metrics such as the deployment success rate or mean time to failure. These metrics give important intel about the stability of your DevOps implementation.

Good luck with your DevOps journey!

This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!

Log Forensics: The Basics of Finding Intel in Your Logs

Today’s post covers yet another log-related concept: log forensics. What’s this, and why should your organization care about it?

Well, this is a topic related to logs, which are ubiquitous in the technology field. An IT organization that doesn’t generate many MBs worth of logs each day would be a rare occurrence nowadays.

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Even though logs are omnipresent, specific terms might not be so well-known. Not long ago, we covered log analytics, and today it’s log forensics time.

This post starts similarly to the log analytics one, by providing a brief overview of logging and its value to an organization. If you have experience with logging, you can skip this section without missing anything.

Then we get to the meat of the post, where we define log forensics, explain what it’s used for, and how it differs from other approaches, such as log analytics. Let’s get started.

The Value of Logging

As promised, before we start covering log forensics itself, we’ll do a quick overview of logging. What is logging all about?

In a nutshell, logging consists of recording information about what a system—for instance, an application—does when executing. We write these recordings to some persistent medium, such as a database table or a file in the disk. That way, we can access such information afterward and review it, but why would that be a desirable thing?

The primary use for logging of any kind is troubleshooting. Reading through log entries gives you this sort of time-travel power. By reading application logs, for instance, you can retrace the actions a user performed in an application, so you can understand and fix a problem.

Using logs only that way, though, really amounts to a reactive use. Don’t get me wrong: using logging to understand and fix problems is an amazingly useful approach. But at the end of the day, you’re leaving money on the table if you can’t put your logs to work for you in a more proactive manner.

That’s where approaches like log analytics come in handy since they allow you to use your logs in a more proactive manner, by extracting insights for them and potentially preventing problems before they happen.

How exactly does log forensics fit into this picture? How does it differ from log analytics? That’s what we’re going to see next.

Enter Log Forensics

We’ve just given you a quick overview of logging. Now you understand what logging is and why it matters if you didn’t know that already.

With that out of the way, we’re ready to get to the topic that gives the post its name: log forensics.

Log Forensics: How to Define It?

Log forensics, in a nutshell, consists of logging analytics applied to computer forensics. Let’s break that down by first explaining what we mean by computer forensics.

Defining Computer Forensics

SearchSecurity defines the term as follows:

Computer forensics is the application of investigation and analysis techniques to gather and preserve evidence from a particular computing device in a way that is suitable for presentation in a court of law. The goal of computer forensics is to perform a structured investigation while maintaining a documented chain of evidence to find out exactly what happened on a computing device and who was responsible for it.

Another interesting and shorter definition is this one by Anton Chuvakin:

Computer forensics is (the) application of the scientific method to digital media in order to establish factual information for judicial review.

So, we could think of computer forensics as an interesting intersection between technology and the law. In other words, computer forensics consists of putting technical knowledge in service of the law.

Defining Log Analytics

Now we’ll offer a brief definition of a term we’ve mentioned a few times throughout this post: log analytics.

We actually have a whole post on the topic, and we recommend you read it. But in any case, here you have the definition we gave to log analytics there:

Log analytics means taking an active approaching to logging, instead of a passive/reactive one. It means analyzing log entries to better understand our applications, to be one step ahead and solve problems as quickly as possible—or prevent them before they happen.

Since log forensics involves log analytics, the definition above makes it obvious that log forensics is much more involved than just looking at log entries.

Since we’ve already defined both computer forensics and log analytics, we’re ready to put the two together to come up with a proper definition of log forensics.

Putting the Two Together

Log forensics is, in short, the fusion between computer forensics and log analytics. Putting the two concepts together, we can come up with a definition like this:

Log forensics means using log analytics in forensics. In other words, it means to perform analysis on log entries, in order to extract knowledge and insights from them, but with a very specific goal in mind: to establish factual information for judicial review.

I’ve seen log forensics be described as an intersection between law, science, and technology, and that’s not a bad way to put it. We could think of log forensics as a specialization of log analytics geared toward judicial investigations.

So, what is log forensics useful for? Does your organization need to care about it? That’s what the next section will cover.

Log Forensics: What Is It For?

We’ve just defined log forensics, but you might still be wondering what its applications are. It all boils down to security.

In short, log forensics is part of a response to incidents strategy. “Incident” here refers to security incidents. Those might be harmless, routine events, but might also be more severe occurrences, such as hacks or crimes.

What follows is a non-exhaustive list of scenarios in which computer forensics—and log forensics more specifically—might be essential:

  • Finding the vulnerability which was exploited to allow an invasion
  • Finding proof of a crime or hack
  • Enabling data recovery from disasters
  • Tracking the activities of a malicious actor

Log Forensics vs. Log Analytics

Finally, as mentioned before, we’ll understand how log forensics differs from log analytics.  The two approaches are meant to extract useful insights from logs, so these can be used to solve or even prevent problems and help in decision making. So, what is the difference, if there is any?

Basically, the difference resides in the overall goals of the two approaches. Log analytics means just analyzing the logs to learn something. Log forensics, on the other hand, has judicial purposes.

In other words, we can think of log forensics as a specialization of log analysis. And since log forensics is also a form of computer forensics, you can expect a greater degree of severity, norms, and regulations. And that’s because, as we’ve mentioned, computer forensic belongs to the realm of law. It’s become an area of scientific expertise, with accompanying coursework and certification.


In today’s post, we’ve defined yet another term related to logging: log forensics.

We’ve started by defining logging, explaining what it is and why it’s so valuable for organizations. Then we’ve proceeded to define log forensics as a sort of fusion between computer forensics and log analysis. Then, we’ve defined both computer forensics and log analysis and ended by putting the two together to come up with the final definition for log forensics.

After that, you’ve seen what are the uses for log forensics, learning that it amounts to log analysis applied to computer forensics. In other words, technology serving the law. We’ve wrapped up by explaining how log forensics differs from log analytics.

That’s it for today. Stay tuned to this blog to learn more about logging-related concepts, since that’s a common topic around here. Also, don’t forget to take a look at XpoLog’s product, which is a complete tool that automates the entire log management lifecycle: from log collection to problem discovery, passing through parsing, analysis, and monitoring.

What Is Log Collection? The Value of Unifying Your Logging

If you work in IT, especially on the decision-making side, then you should be aware of how vital logging is. When things go wrong with software in production, a solid logging approach often is the difference between hours of fruitless troubleshooting and an easy fix. Today, we’re here to talk about logging by covering a specific angle. Namely, we’ll answer the question, “What is log collection?”

Why is this an important question to answer? As it turns out, a typical, modern tech organization will have many types of logs, written to an equally large number of targets, or destinations. We’re here to argue that, although there’s certainly a reason and a place for each of these log types, you can also find benefits in unifying them. That’s what this post is about.

We’ll start with the basics. Before getting into log collection, we need to be on the same page regarding logging itself, so that’s where we’ll begin: by defining logging, explaining why this is such a big deal, and covering the benefits it provides. Then, we’ll proceed to cover the variety one can find in the logging world. We’ll cover the types of logs, explaining the reasons for each one.

Then, at last, we’ll get to the meat of the article. By answering the title question, we’ll explain why there is value to be found in unifying your various log sources. We part ways with some final considerations and tips on tools you can use to unify your logging approach.

Let’s get started.

Log Collection Basics

As promised, we’ll start with some definitions. The first one of them is a definition of logging itself. Feel free to skip ahead if you’re already experienced or are at least familiar with logging.

What Is Logging?

So, what is logging? In short, logging is the practice of creating and maintaining logs. A log is a recording of an event of interest that happened in a computer system or application.

It’s common to write log entries to text files on the disk, but that’s not the only available option. It just happens to be one of the most popular ones. As you’ll see later on this post, you can write logs to a variety of destinations, such as database tables, email, cloud services, and many more.

Since we’ve just covered the “what,” it’s time to get to the “why.” Why do people bother logging? What do they expect to get from it?

We Use Logging Because Tech Is Complex

Why bother logging? The TL;DR version is tech is complicated and things break often.

Now, for the slightly longer answer.

IT is complex. Let’s take software development as an example.

Developers write code, along with unit tests, to verify its behavior. When they’re done, they check in their changes to a CI server. The CI server will then build the application, resolving whatever dependencies that code requires, besides running the unit tests and maybe additional types of automated tests.

Depending on the type and size of the organization, the code might be handed to a QA department, which will then submit the application to myriad types of tests, both manual and automated. When they’re satisfied, the application might still need to go through an acceptance phase, in which a stakeholder gives their sign-off.

Even with all those verifications and tests done at each stage, there’s still no guarantee the application will work as intended when it gets to the production environment. When things do go wrong, the programmers assigned to fix the problem will need to know, among other things,

  • The exact date and time the problem occurred.
  • In which part of the application the problem happened.
  • Details about the user session in the application.
  • The stack trace of the error or exception that occurred.

And what would be a sure way to get these pieces of information where they need to be? You’ve got it: logging.

Logs in All Shapes and Sizes

In order to understand why it might make sense to unify your logs from various sources, you must first understand what all those sources we keep telling you about are. That’s what this section covers.

We’ll start by covering some of the different types of logs an IT organization might employ. After that, you’ll see some of the most often used destinations (or targets) for logs.

Types of Logs

What follows is a list of existing types of logs. It’s not an exhaustive list, but it features the most common types of logs you’re likely to find at your company.

Application Logs

Application logs are simply logs that are generated by the various applications you happen to use. Their main purpose is to allow maintenance programmers to perform an after-the-fact debugging session of the application. It’s sort of like a time machine: by reading through the log entries generated by the application, the developers can understand how it behaved in production and, hopefully, detect and fix whatever is causing it to malfunction.

Detecting errors isn’t the only thing application logs are good for, however. Logging can also help you answer questions that are of interest to the business, such as:

  • What are the most and least used features?
  • How many visitors does each page get per day (and other time periods)?
  • How much time do visitors spend on each page or feature?

In short: application logs are useful to detect and fix problems and also to help the organization gather data and insights that can be useful to improve the application.

System Logs

System logs are another important type of log. Unlike application logs, system logs are generated by the operating system itself—hence the name.

Most entries you’ll find in the system log are written by the operating system itself, but applications can also add log entries there.

On Windows, the system logs are called the event log, which you can view through the Event Viewer application. If you’re using Linux, the system log consists of the Syslog service and its successors.

Finally, if you’re a macOSX user, you can access the system logs by using the Console app.

Web and Application Server Logs

The final log type we’re covering today is the log generated by an application or web server.

Web servers such as Apache, IIS, and NGINX generate log files that are essential to troubleshoot problems with websites or web applications, and the same is true for application servers.

Common Logging Destinations

You’ve just read about three of the most common types of logs. Now you’ll learn about some of the most common destinations or targets for logs.

Text Files

It’s very common to write logs to plain text files. Text files are very approachable. They’re cheap, easy to use, and ubiquitous. You don’t need fancy software to read them; a common text editor will do. Except for nuisances such as differences in end-of-line characters between Windows and *nix systems, text files are intrinsically cross-platform.


Database tables are another common destination for logs. The main justification for picking a database as a target for logging would be the ease of querying and the other relational facilities, such as relational integrity.


Emails are yet another interesting target for a log. By redirecting the log to a specialized email address, you could, for instance, automate the opening of tickets when a problem happens in production.

Cloud Services

An increasingly popular alternative is to integrate logging targets with cloud solutions. For instance, you could have a logging target that writes log messages to providers such as Amazon CloudWatch Logs, Azure Storage, or Google Logging.

Log Collection; AKA Why Unifying Your Logging Is Worth It

As you’ve seen so far, logging can take many forms. Logs vary in their types, formats, and destinations. They vary in their severity levels, intended target audience, and in how often they get written.

When you take all of that variety into account, it doesn’t take long until you realize that a comprehensive logging approach can be an incredible asset for the business. Analyzing a large number of log entries can be a powerful way of obtaining insights about pretty much all facets of an IT operation.

However, all of those precious insights might be out of your reach if your logs are all scattered through a thousand different places.

That’s when log collection comes in handy. By having an automated process that collects logs from varied sources and aggregates them into one place, you have the best of both worlds, so to speak. For starters, you don’t need to change anything to the way you currently do logging. In other words, it’s the log collection process that will adapt to your already existing logging approach and not the other way around.

And then you get all of the benefits of having your logs concentrated in a single place. Having all logs from the organization at your fingertips will allow you to obtain insights that wouldn’t be possible otherwise.

You could, for instance, analyze web server logs along with application logging, to determine whether there’s a correlation between the server’s performance and the time users spend on the application. That’s just an example, but the potential is limitless.

What Now?

Let’s do a quick summary of what we’ve seen today.

We’ve learned what logging is, why it’s important, and the benefits it brings to an organization. You’ve learned that logs come in many shapes and sizes, different by their types and destinations, among other properties.

Getting all these myriad types of logs together in one place gives you the ability to obtain insights that wouldn’t be possible to get otherwise. That process is what we call “log collection,” and today you’ve learned why it is valuable. Where to go now?

First, don’t stop learning. Continue reading articles, on this blog and elsewhere, to learn about logging and related topics. Also, research and learn about tools at your disposal. For instance, take a look at XpoLog’s offering, which is a full-fledged log management tool, powered by AI and completely automated.

Thanks for reading, and see you next time.

What Is Operational Analytics? A Practical Introductory Guide

What Is Operational Analytics? In this practical introductory guide we will give a definition to the term but not without explanations about why you need to practice operational analytics and how.

We will review some alarming statistics and will show you how you can remove yourself from being included on the wrong side of the diagram.

We will finish the article with a real-life example and show you how XpoLog can help in turning your corporate data into actionable insights (and thus enabling operational analytics strategies).

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Let’s begin

Cloud computing, mobile devices, and IoT technologies continue to evolve and proliferate. As a result, businesses are generating and collecting more data than ever before.

Data is generated and stored every time a customer interacts with a website or device. Savvy companies understand the importance of capitalizing on that data. It enables them to enhance customer experiences and increase profitability, among other countless benefits.

At the same time, every day an employee uses a company-issued tablet or device to do their jobs, they generate data. And every purchase—whether that’s coming from customers or the procurement department—leaves a trail of data, too.

Suffice it to say that in the age of big data, leading companies need to analyze this data—rapidly and easily. That’s the ticket to increasing workplace efficiency, driving competitive advantage, and delighting customers.Many companies are having a difficult time harnessing all of the data they generate.

Struggling to Make Sense of It All

As it turns out, however, many companies are having a difficult time harnessing all of the data they generate. That’s due to a confluence of factors. For example, data often lives in several different repositories. And many workers need to get IT’s help to run analytics.

Believe it or not, a recent study found that 55% of the average business’s data is unused. This means that it has no practical value. Since the data is unused, it’s impossible to know what trends and insights are hiding in it.

While lots of businesses have a hard time using their data to begin with, many other organizations struggle with data integrity. In fact, one recent study found that the financial impact of poor quality data is expected to exceed $3.1 trillion annually in the U.S. This makes perfect sense: When you’re making decisions based on inaccurate data, how can you expect to make the best ones?

Still, the writing is on the wall; 81% of technology professionals agree that data is very valuable to their organization’s success.

A Better Way Forward

The good news is that—with the right tools in place—it’s possible to tap into all this data. And leverage it rapidly to make better business decisions.

For example, companies can use a process known as operational analytics to analyze large swaths of data and figure out the best path forward.

If you’d like to learn more about operational analytics, you’ve come to the right place. Keep reading to learn more about what operational analytics is, how it can be used, the benefits of operational analytics, and more.

What Is Operational Analytics?

Operational analytics is the process of using data analysis and business intelligence to improve efficiency and streamline everyday operations in real time.

A subset of business analytics, operational analytics is supported by data mining, artificial intelligence, and machine learning. It requires a robust team of business and data analysts. And it also requires the right tools (think Tableau and Looker).

As such, operational analytics is much better suited to large organizations than small businesses—at least for now.

Now that we’ve got our definitions out of the way, let’s take a look at some of the transformative benefits operational analytics delivers.

Operational analytics can have a profoundly positive impact on the entire enterprise.

What Are the Benefits of Operational Analytics?

There’s a reason leading organizations are increasingly investing in operational analytics. It can have a profoundly positive impact on the entire enterprise.

Here are three of the reasons why businesses that prioritize operational analytics don’t look back.

1.Faster Decision-Making

Quite simply, businesses that can analyze and react to customer data in real time are able to make much faster decisions.

Traditionally,  businesses would make adjustments to their operations based on a quarterly or annual data review. In this reactive manner, they might miss out on serious revenue or glaring issues. They’d only become aware after the fact.

On the other hand, companies that embrace an operational analytics platform can make adjustments to processes and workflows in real time. Or at least close to it. As such, they are in a better position to increase profitability and reduce waste. They can also detect problems and inefficiencies quickly and respond to them rapidly.

In fact, one recent study found that improving operations can result in a $117 billion increase in profitability for global organizations.

2. Enhanced Customer Experiences

Businesses that react to situations in real time are able to provide better customer experiences. It’s that simple.

For example, imagine an e-commerce company runs operational analytics. After that, they find that a significant percentage of its users are adding items to their carts but not completing transactions. Armed with that information, they then investigate the issue. It quickly becomes apparent that their website is buggy and checking out is a nuisance.

After identifying what’s wrong and fixing it, the company improves the customer experience and drives more online sales.

3. Increased Productivity

Thanks to operational analytics, businesses can see the inefficiencies that exist in their workflows. Accordingly, they can then change their processes to streamline operations.

For example, a company might run analytics and realize that the process for approving a purchase order is too cumbersome. In this case, it requires too many signatures from too many people who are moving around constantly.

This data might encourage them to rethink the process entirely. They may either decide to reduce the number of signatures required to approve a PO. Or they could opt to move to an online system that eliminates the need for having to track anyone down in person.

Now that you understand some of the benefits of operational analytics, let’s take a brief look at a real-world example.

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A Real-Life Example of Operational Analytics in Action

Let’s say you want to install an in-home, cloud-based smart security system. You order the product online and it arrives a few days later. After you pop open the box and glance over the instructions, you plug in the device.

But, for some unknown reason, you’re unable to connect it to your local WiFi network. The device is trying to connect. But the signal keeps cutting out.

An enterprise with a robust operational analytics platform might be able to detect that you’re trying to connect for the first time. Then an alert would appear in central management’s operational analytics platform indicating a new device is being activated. The platform would also indicate that the device connection isn’t steady. Instead, it’s turning on and off.

As a customer, you’re becoming increasingly frustrated. The cool new device you just paid a pretty penny for isn’t connecting—despite following all the instructions. Further, you only have a few minutes left before you need to head to an appointment.

Under the old way of doing things, companies would expect customers to call a support line. At this point, the already frustrated customer would then have to wait on the phone. Finally, if and when a live agent picked up, they would troubleshoot the issue over the phone. Or, if that didn’t work, they’d schedule an in-person technician appointment.

Customers tend not to like either of these approaches. What’s more, they’re wasteful to businesses, too.

Operational Analytics Saves the Day

Now, here’s how operational analytics can truly enhance customer experiences. Rather than taking this reactive legacy approach, the security company could proactively reach out to the customer. They could provide personalized customer service by sending a text message to the customer asking if they need help. Or, better yet, a real person could call the customer and walk them through the setup process.

This is a big deal. A recent study indicated that 83% of U.S. consumers prefer working with a real customer service person to resolve issues. At the same time, another study showed that customers who experience personalized customer service are 44% more likely to return.

Think of the transformative change this proactive customer service approach can have on customer experiences—and on profits.

Is Your Business Ready to Use Operational Analytics?

By now, you probably know how important it is for your business to embrace operational analytics. If you’re looking for a turnkey platform that can help your business be more proactive and more efficient, check out XpoLog’s Application Monitoring Platform.

With features such as AI-powered log analytics and real-time monitoring of performance issues and cyber threats, you can rest easy knowing that your business is running smoothly at all times.

That’s the ticket a stronger business, happier customers, and a healthier bottom line.

What Is Log Correlation? Making Sense of Disparate Logs

Before the need for log correlation, there was a time not so long ago when reading software application logs was simple. Your application would output log files in sequential order, and you’d read through them. In the event of a bug, software outage, or security incident, you could easily parse what happened and when. It was a tedious process, but it was simple.

That’s not the case any more. In the interest of scaling our software to service millions of users, that software is significantly more complicated. In today’s software landscape, logs originate from dozens of sources. Even a relatively simple web application has client logs, logs from a load balancer, logs from the web server, database logs, and logs from worker services that handle longer-running tasks. If your application has even slightly more advanced capabilities, your logging requirements expand considerably. Every new feature requires more logging in order to monitor correctly.

So how do we handle all these logging demands? That’s where log correlation comes in. What is log correlation, you ask? Keep reading. In this post, we’ll define log correlation and talk about why it’s so important.

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In today's software landscape, logs originate from dozens of sources

When Something Goes Wrong, Challenges Arise

One of the negative side effects of this level of complexity is that troubleshooting issues is complicated. Few bugs are simple, but complicated architectures mean that even pinpointing a bug is time-consuming.

As user activities propagate between different parts of the system, they generate log events. As an engineer, you can track down those log events, but it’s going to take some time. On busy systems, there’s an extra layer of complication. Similar application actions will generate similar log entries. Do you know if that database log entry came from the bugged request, or a similar one that completed successfully?

Sometimes, the challenge can come from just finding the logs. Where does your web server log to? What about your database? Managing logs in modern applications can feel like trying to solve a maze in the dark when you don’t know if there’s an exit.

This is the complexity we’re talking about. Eventually, you’ll figure out where each log entry came from, and you’ll be able to sort out the root cause of your bug. That’s good! But it’s likely that it’s going to take you hours, and when there’s a production bug, your bosses aren’t likely to be excited by hours before you even know what the bug is.

This delay is even more significant when dealing with a suspected security event. When you think someone has compromised your system, a few hours can be the difference between repelling them and a major security breach.

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Different Services Log Differently

Another significant complication stems from the fact that different services have different log formats. In the heat of the moment, you want someone who’s an expert on the service experiencing the bug. But reading Apache logs is a lot different from reading log output from Postgres. Someone who’s an expert in Apache might have to guess what’s happening with a log from another application. This amplifies the difficulty of tracing an event through your system’s logs. Each level of communication overhead introduces another place where your process can fail. Each failure pushes back the time necessary to resolve your problem.

A few hours can be the difference between repelling attacks and a major security breach.

Log Correlation Tackles Those Challenges

Thankfully, you don’t have to live with all of this complexity. Log correlation software, like Log Management from XPLG brings all of your logs into a single location. By moving all logs to a centralized location, a significant element of complexity is removed. When something goes wrong, you know right where to look.

But log correlation software isn’t simply the act of bringing all logs into a singular location. While that’s helpful, log correlation is much more powerful than just that.

Tying the Threads Together

Instead, log correlation fills a much more valuable purpose. It’s able to track actions throughout your system and trace the logs they generate. That’s the “correlate” part of log correlation. Under the hood, log correlation is a terrific bit of engineering. Application programmers build pattern-matching software which is able to direct the software to determine which parts of disparate logs represent the same action. Quality log correlation software comes with these rules built in by default. It’ll also let your team define custom rules. All that hard-earned knowledge about how to trace an action through your system is fed directly into the system. Instead of hours spent tracking actions through the system, the same logic is executed in milliseconds.

Working Automatically With Different Systems

One significant benefit of software like XPLG is that it features a robust application store where you can add log plugins based on the services you use. Because these parsing tools are written by experts in their technology, picking up one of these plugins is like adding that expert right to your debugging team. Now, you eliminate that communication overhead. Your team doesn’t need to constantly talk back and forth to figure out where an event originated. The logs are effectively sequenced in order from the start of an event to the end. You’re able to quickly and smoothly determine how a bug traced through your system, and root out the cause in minutes.

What’s more, slight configuration differences in systems can cause big problems in log collection. An effective log correlation service will help smooth over those bumps so you’re getting the right data, every time.

The Next Level: Log Analysis

When you run an application that serves millions of users, waiting for a bug or security event to announce itself means you’re behind. Especially with the case of security events. As we noted before, every minute lost when an intruder is trying to compromise your system is the potential for real trouble.

Instead, log correlation and management systems can alert you when unexpected behavior is happening, before a user even knows something is wrong. You’re able to see the overall health of your system in real time. For instance, imagine that a network link between your web server and database is severed. In a traditional logging environment, you don’t know about this problem for some time. You first need to receive reports that requests are failing. Then you need to begin tracing each of those events through disparate system logs. It won’t be until you’ve done two or three that your team will suspect there’s a network outage issue.

You can fix the problem before someone even knows something is wrong.

Instead, with an effective log correlation system, you’ll be able to recognize that a high percentage of requests are failing to reach the database in just minutes. The time saved turns an outage of hours into one that takes minutes to resolve. Log correlation systems can even provide configuration options to automatically notify team members when an unexpected number of errors occur. It could be that the first time you learn your network is down isn’t from a customer, but from an automated system. You can fix the problem before someone even knows something is wrong.

Log Correlation Lets You Focus on What’s Important

In today’s software world, complexity is everywhere. It grows constantly, and it’s nearly impossible to avoid. Every new feature request means adding a new layer of complexity to your software. Most of the time, this doesn’t impact your team. When things are running well, complexity isn’t a source of stress. But when things break, that complexity can feel back-breaking. Log correlation is a tool to reduce the weight of that complexity. It provides real ways to simplify how you visualize data flowing through your systems. When implemented correctly, it even helps your team take action proactively. Best of all, XPLG makes it easy to start, for free. What are you waiting for?

What Is MTBF? Mean Time Between Failures Explained in Detail

Time for another installment in the series where we explain in detail yet another important metric for tech organizations. After covering MTTD and MTTF, today we answer the question, “What is MTBF?”

As the post title makes clear, MTBF stands for “Mean time between failures.” The acronym refers—like the others that came before it—to an important DevOps KPI. But what actually is it? What is it good for? How do I implement it? Today’s post features the answer to all of the above questions—and more.

The rest of the post will go deeper into MTBF. First, you’ll learn its definition. After that, you’ll see the justifications behind its use. After taking care of the “what” and “why,” it’s time to cover the “how.” We’ll do that by explaining how to calculate this metric, using two simple yet realistic examples.

Then we continue by mentioning other metrics that are essential for DevOps and are intimately related to our topic. After that we wrap-up with a quick summary of the post along with some final considerations. Let get started!

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What Is MTBF? A Quick Definition

Mean time between failures probably has the most intuitive name of all the metrics we’ve covered so far. It means pretty much what you would’ve guessed by its name: it is the arithmetic mean (average) amount of time between failures of a mechanical or electronic system. The metric usually doesn’t include repair times. MTBF values are usually used to predict how likely a single item is to fail within a given timeframe.

Calculating MTBF should be simple enough. All it takes is to look at a failures report for a given system, get the times between the different failures, and then calculate the average time.

Why is this metric so important? We’ll answer that in more detail in the next section. For now, the short answer is that MTBF is a reliability metric. It allows you to determine how reliable a given piece of equipment or other item is, so you can make informed decisions about them.

Why Should You Care About MTBF

In the previous section, we’ve briefly mentioned some of the reasons why MTBF is a worthwhile metric. But now we’re going to dive a little bit deeper on the issue. So, what’s the deal with this metric? Why should your organization care about it?

As you’ve seen, MTBF is a maintenance metric that indicates how long a system can work until it fails. So, this metric is closely associated with the availability (or uptime) of the given equipment or system. Uptime is also a key indicator for organizations. Through it, organizations can determine the overall effectiveness of a piece of equipment of any other system.

It’s important to keep in mind that MTBF is a metric that only applies to repairable items. For non-repairable items, the metric you should use is a different one: good old Mean Time For Failure (more on that in a minute.)

MTBF is a crucial metric to use when evaluating equipment and other systems before a purchase. Since the metric calculates the average length of time between issues, it is an indicator of reliability. Based on the metric, you can have a really good idea of how long a given system works before it stops working. By tracking the metric, you’ll be able to make educated decisions on a lot of areas like safety, maintenance schedules, and many more.

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People often mix “Mean time to failure” with “Mean time between failures.” And to be fair, people who think these two are the same are almost right.

As you’ve seen, MTBF is the metric you use for repairable items. When it comes to non-repairable items, it doesn’t make sense to say “failures” in the plural. A non-repairable item fails just one time, and then it gets replaced. So, in scenarios like that, one should use “Mean time to failure.”

Calculating MTBF in Practice

Let’s now show you how to calculate MTBF. It’s probably easier than you’d think. You just take the total time the item is running (i.e., availability of uptime) and divide it by the number of failures that happened over that time frame. We’ll now see a quick example of how to calculate this metric.

Suppose you have a mechanical machine created to operate 12 hours per day. Then, let’s say it breaks down after operating for four days. The MTBF, in this case, is 48 hours.

MTBF = (12 hours per day * 4 days) / 1 breakdown = 48 hours

The example above is as simple as it can get. The calculation can and does get more steps. For instance, when you analyze longer amounts of time in which several problems happened, that asks for more steps when calculating MTBF.

Let’s make our example slightly more complex. Suppose the same piece of equipment, running for 12 hours per day, breaks down twice in the span of 8 days. The first problem happened 20 hours from the start time and took 2 hours to repair, which means our first session of uptime lasted for 20 hours.

The second problem occurred 50 hours from the start time and took 4 hours to repair. Since the first issue occurred after 20 hours of uptime and took 2 hours to repair, that means the second problem occurred after 28 hours of uptime (50 – 22 = 28.)

The second problem took 4 hours to repair, which means we’re left with 42 (96 – 54) hours of uptime. Now comes the easy part: to plug the values into the MTBF formula:

MTBF = (20 hours + 28 hours + 42 hours) / 2 breakdowns
MTBF = 90 hours / 2 breakdowns = 45 hours

As you can see, the mean time between failures is equivalent to the total uptime for the given item.

Other Metrics to Be Aware Of

MTBF is a key indicator to determine the reliability of systems. Here are some other important metrics you should check out:

  • MTTF (Mean time to failure): The amount of time a product/device/system is expected to work.
  • MTTR (Mean time to repair): The time it takes to fix an issue after its detected.
  • MTTD (Mean time to detect): The time the organization takes to detect an issue.

Final Considerations

In this post, we’ve answered the question, “What is MTBF?” We’ve started by defining the metric, explaining what it means and why it’s important. Then we’ve cleared a common misconception regarding MTBF and MTTF. After that, we’ve proceeded to show how to calculate MTBF in practice using a real example.

Where do we go now? Well, knowledge is an investment that always pays itself handsomely. So what about studying? Read about DevOps key metrics on this blog and other sources. Read about other topics as well. Also don’t forget to take a look at available tools that can help you.

For instance, consider XpoLog. XpoLog help to optimize MTBF with:

  • Deep text inspection for log data to discover errors
  • Anomaly detection monitoring for performance, exceptions and errors
  • Pattern analysis and new errors discover
  • Log volumes and flow monitoring.

XpoLog is a fully automated log management tool. It collects, parses, and tags logs from many different sources.

Log management is essential for implementing MTBF and other metrics. Logs are the closest we have to an authoritative source of truth to determine the start times of problems in production, which makes them a really powerful asset that needs to be properly handled and managed.

That’s all we have for today. Thank you for reading and until next time.

What Is MTTF? Mean Time to Failure Explained in Detail

“What is MTTF?” That’s the question we’ll answer with today’s post. Yep, the article’s title makes it evident that the acronym stands for “mean time to failure.” But that, on its own, doesn’t say anything. What does “mean time to failure” actually mean? Why should you care? That’s what today’s post covers in detail.

The structure of this post will mostly follow the template we’ve laid out with the mean time to detect (MTTD) article. We’ll start with the “what” of MTTF, giving a complete definition of the term. After that, we’re ready for the “why”—you’ll learn why you and your organization should care about this metric, understanding all the benefits it can provide. After that, we’ll finally be ready for some practical tips. We’ll invite you to roll-up your sleeves and learn how to calculate MTTF.

Before parting ways, we list other essential DevOps metrics you should also know. Let’s get started.

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Defining Failure

It’s common for me to start posts by offering a definition of its subject matter. This time I’m taking a different route, though: let’s begin by defining “failure.”

You might think that failure is such an obvious concept that it bears no definition. After all, if something doesn’t work at all, it has failed. What’s so complicated about it?

I beg to differ. Things aren’t black and white when it comes to failure, especially in the IT world. Indeed, there can be more granular modes of failure.

Is a car with a flat tire a failure? What about a phone whose touch screen features randomly don’t work? And a musical keyboard which doesn’t produce sound in some keys? Are these examples failures or not? The way I see it, yes. Even though you could say that they “work,” they don’t work at the level they’re supposed to.

A failure, generally speaking, means that something doesn’t meet its goals. Specifically, in the tech world, that usually means a system outage, aka downtime. But there can be scenarios in which, despite not having a full-blown system outage, you can say that there is a failure. You could have an application that performs orders of magnitude slower than it should. That’s failure. It doesn’t matter that the result was technically correct when the system takes more than 24 hours to perform a task that should have taken a few minutes, at the most.

What Is MTTF: A Definition

MTTF is a critical KPI (key performance indicator) for DevOps. As you already know, the acronym stands for mean time to failure. But what does that actually mean? In a nutshell, MTTF refers to the average lifespan of a given item. In other words, it refers to how long a piece of technology is supposed to last operating in production.

MTTF is closely related to another metric—MTBF (mean time between failures.) Well, to be fair, they’re virtually the same thing, with just one important difference. You’d use MTBF for items you can fix and put to use again. On the other hand, you’d use MTTF for items that can’t be repaired. If you find yourself in such a scenario where MTTF is used as a metric, that means repairing the problematic item isn’t an option, so you’ll have to replace them.

Mean time to failure sets an expectation. It represents the length of time you can expect an item to work in production. Keep in mind that when companies calculate the mean time for failure for their various products, they don’t usually put one unit to work continuously until it fails. Rather, this metric is often computed by running a huge number of units for a specific amount of time.

Why Should You Care About MTTF

You’ve just learned the “what” of mean time to failure. Let’s now turn our focus to the motivations behind calculating this metric. Like MTTD, one of the best reasons for calculating MTTF is to improve it. Unlike MTTD, though, this metric improves when it goes up instead of down.

MTTF measures reliability. With it, you can know how long a product typically works before it stops working. Having this piece of data, your organization is able to make informed decisions on important issues, such as inventory management (which even includes from which brands to purchase or not purchase), scheduling of maintenance sessions, and more.

MTTF also helps us, albeit indirectly, to evaluate your monitoring mechanisms. This is a quote from our post on MTTD:

“MTTD also has an additional—and arguably more important—benefit: it serves as a test of your monitoring mechanisms. Here’s the thing—your organization already adopts tools and processes to monitor incidents. If these tools and processes work as intended, it shouldn’t be that hard to keep your organization’s MTTD low. The opposite is also true. If you adopt incident management mechanisms that aren’t up to the task, you and your DevOps team will have a hard time keeping MTTD down, which can result in catastrophic consequences for your organization.”

You could say that MTTF, as a metric, relies on MTTD. In order to track how much time components work until they stop, the organization must be able to detect system outages and other problems. So, by carefully tracking MTTF, you’re also keeping an eye on the health of your monitoring procedures.

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Calculating MTTF

You calculate MTTF taking the total amount of hours of operation (aka uptime) and divide it by the number of items you’re tracking.

Suppose we have four pieces of equipment we’re testing. The first one failed after eleven hours, while the second one failed after nine hours. The third failed after seven hours, and finally, the last one failed at five hours. So, we have a total uptime of 32 hours, which divided by four equals eight hours.

This suggests this particular equipment will need to be replaced, on average, every eight hours.

Other Metrics to Be Aware Of

MTTF is a key indicator to track the reliability of your assets. Here are some other important metrics you should probably know:

  • MTTD (mean time to detect): The average amount of time it takes to detect problems in the organization.
  • MTTR (mean time to repair): The time it takes to fix an issue after its detected.
  • MTBF (mean time between failures): The time the organization goes without a system outage or other issues.

What’s Next?

In this post, we’ve answered the question, “What is MTTF?” Mean time to failure is an important metric you can use to measure the reliability of your assets. Since MTTF shows the amount of time a product, component, or other types of assets usually work until they fail, you want to keep it as high as possible.

When it comes to DevOps, MTTF is one of many important metrics we need to track. Just like MTTD, the previous metric we’ve covered, MTTF serves more than one purpose. The first and obvious one is to be a reliability measure. By tracking the mean time to failure, we understand how reliable our equipment, components, and assets are, so we can make more educated decisions. But MTTF can also help us to evaluate the effectiveness of our monitoring solutions because we have to detect outages in order to measure the time between them.

Where to go now? Well, keep searching for more knowledge. Learn about other important metrics. Learn about tools that can help you with such metrics.

For instance, take a look at the fully automated log management tool XpoLog. It collects, parses, and tags logs from many different sources.

XpoLogs’ ML-powered engine adds layers of intelligence over your searches, it automatically and proactively detects errors and allows you to prevent outages and meltdowns. XpoLog contains a leading analysis apps marketplace with thousands of ready-to-use-reports and dashboards, to extract actionable insights immediately, in real-time. Download XpoLog now and improve your monitoring mechanism.

Log management is essential for tracking metrics such as MTTD and MTTF since logs are very reliable sources of information when it comes to system outages.

That’s it for today. Thanks for reading.